According to the known prior art, IOLs are selected and/or adjusted on the basis of measured and/or estimated variables, wherein only individual parameters in the form of single measurement values or as a mean value from defined patient groups are taken into account. However, the dependencies from the specific concomitants of the treatment, such as characteristics of the patients, diagnostics, surgical method and the like, as well as the use of statistical distribution for the parameters are not taken into account.
The selection according to the known prior art can be described in accordance with FIG. 1.
The biometric data of the eye to be treated, which are determined using an ophthalmological measuring device as well as the data of the (1 . . . n) IOLs eligible for implantation are the input parameters for the calculation process.
These IOLs typically vary according to IOL type (including variation of their asphericity or toricity) and refractive power of the IOL.
With the use of a calculation model (typically an IOL formula or by means of ray-tracing), an output and/or evaluation parameter (typically, the refraction of the patient after implantation of the IOL) is calculated in the next step. This output and/or evaluation parameter is then optimized through variation of the input parameters selectable by the physician such that the target refraction is obtained.
The currently most prevalent calculation models are so-called IOL formulas, e.g. according to Holladay, Hoffer, Binkhorst, Colenbrander, Shammas, or SRK.
Accordingly, the refraction D (=output/evaluation parameter) of the patient after implantation of the IOL is calculated asD=DIOL−f(K,AL,VKT,A)  (F1)
wherein f( ) is a classically known IOL formula
and DIOL refractive power of the IOL,                K the measured keratometry value,        AL the measured axis length of the eye,        VKT the measured anterior chamber depth, and        A an IOL type-dependent constant, are input values.        
For selecting the IOL, the physician predetermines a target refraction (D=DTARGET). For the optimization, the physician calculates the refraction according to (F1) for different IOLs through variation of DIOL and A. In many cases, the physician uses IOLs of the same type, so there is no variation in A, and the optimization amounts to a formula calculation according to DIOL=DTARGET+f(K, AL, VKT, A). In case of emmetropia as the target, the classical formula calculation of the IOL is therefore DIOL=f(K, AL, VKT, A).
The constant A in the formulas is determined empirically using a patient group in order to adjust the formula values to the actually resulting optimal refraction values. However, this adjustment merely ensures that the mean value of the refraction values for the test group corresponds with the formula.
Statistical errors of the biometry formula are typically taken into account by the physician such that the physician knows from experience that the actual obtained refraction values for a patient have a certain variation in the target refraction. If the physician wants to minimize their influence, the physician makes a correction to the target refraction. For example, if the physician encounters deviations of +/−0.25D when compared to the target refraction, which is typical for patients with myopic eyes, the physician will aim for a refraction of −0.25D in order to avoid a high probability for the eye of the patient to become intolerably hyperopic. This method is a good strategy for the mean value of the patient group.
However, the typical deviation of the target refraction and/or the margin could be reduced if single input parameters of the individual patient were used as an output variable instead of a mean value from a patient group.
In order to avoid systematic errors, various approaches are currently used according to the prior art.
A number of physicians use a different A-constant for every ethnic group of their patients. This reduces systematic errors and, provided the statistical scattering in the respective group is smaller, also statistical errors.
Depending on defined initial conditions, e.g. patients with long axis lengths or previous refractive cornea surgery, other physicians use different biometry formulas which are better adjusted to the respective conditions or which require the measuring of additional parameters, such as anterior chamber depth or lens thickness. This also reduces particularly the systematic errors, however, statistical errors may increase in part because of the additionally measured parameters.
A somewhat smaller number of physicians use ray-tracing methods, according to P.-R. Preussner and others in Preussner, P.-R. u. a.; “Vergleich zwischen Ray-tracing and IOL-Formeln der 3. Generation” (Comparison between ray-tracing and IOL formulas of the 3rd generation), Ophthalmologe 2000, 97:126-141, the contents of which are incorporated herein, as a calculation model instead of simple formulas (FIG. 1). Based on the individual measurement values and estimated variables, particularly the position of the IOL in the eye, an eye model is developed with usually several optically active surfaces and “calculated” according to methods from optics design for one or more rays. The imaging quality on the retina/fovea is calculated as an assessment value. If the input variables are determined with appropriate accuracy, systematic errors can by and large be avoided. However, statistical errors, e.g. due to a lack of reproducibility of the measurements or deviations of the wound healing process, are once again not taken into account.
Some manufacturers of IOLs attempt to compensate for the latter deviations by designing the IOL in such a way that the “active” refractive power of the IOL in the mean, i.e. average eye, is as position-insensitive as possible. Such artificial eye lenses and a method for their improvement are described in WO 2007/128423 A1 the contents of which are hereby incorporated by reference herein. Here, the surface shape of the IOL is modified such that it has a surface shape deviating from the perfect sphere. Thereby, the design of the IOL takes into account the natural optical configuration of the human vision apparatus, e.g. visual axis tilt and pupil decentration. In addition, the design method can account for potential positioning errors caused by implantation and surgery effects. However, deviations in the position of the IOL can ultimately not be taken completely into account or compensated.
According to Warren Hill and Richard Potvin in “Monte Carlo simulation of expected outcomes with the AcrySof toric intraocular lens”, BMC Ophthalmology 2008, 8:22, the contents of which are incorporated herein, a Monte Carlo method/simulation can be used for optimizing the selection of toric IOLs with the objective of optimizing the target margin. Similar to the above-mentioned example, the physician is advised, according to Hill and Potvin, to account for a (negative) residual toricity of the “cornea—IOL” system in order to avoid (under-) correction.
In order to arrive at this recommendation, Hill and Potvin calculate the mean postsurgical astigmatism of the “cornea—IOL” system from individual keratometer measurements, from the degree of the IOL selected according to a certain selection strategy, from mean position data of the markings on the eye, from the mean variable of the induced astigmatism, and from the mean postsurgical rotation of the IOL.
This is compared to an actual astigmatism obtained from a simulation. Thereto, the distributions of the induced astigmatism, the marking and the IOL rotation due to measurement uncertainties and deviations in execution and wound healing are used to (purportedly “exactly”) simulate a postsurgical astigmatism according to the Monte Carlo method for each of the 2000 patients of the group. The simulated measurement values of the patient group are subsequently averaged separately for every degree of toricity in order to determine the actual/simulated postsurgical astigmatism. According to Hill and Potvin, this procedure is executed for two selection strategies, wherein ultimately the one strategy is selected which has, as a result, a lesser actual/simulated, postsurgical astigmatism averaged from the patient group.
Hill and Potvin thereby show that distributions in patient groups generated with Monte Carlo methods can be used for the selection of the method and optimal IOL (margin). However, Hill and Potvin neither makes reference to the distribution functions generated with the Monte Carlo methods for the individual patient instead of the patient group nor does it constitute an alternative.
Peter N. Lombard and Scott K. McClatchey describe in “Intraocular lens power requirements for humanitarian missions” J. Cataract Refract Surg Vol 25, October 2009, the contents of which are incorporated herein, how the number of types of IOLs to be provided for humanitarian missions can be optimized using the Monte Carlo simulation. Even though priority is given to the number of allocated lens types, the necessity of implementing regional, cultural, gender-specific and age-specific variation for the selection of the IOL types is explicitly apparent.
The solutions known from the prior art are disadvantageous because only individual parameters in the form of single measurement values or as a mean value from defined patient groups, but not their dependency on the individual patients and/or the specific concomitants of the treatment are taken into account for the selection or adjustment of the required IOL and/or the selection of the optimal surgical parameters.
The possible connections between the selected IOL and the surgical method selected for the implantation was examined by T. Iwase and K. Sugiyama in the study “Investigation of the stability of one-piece acrylic intraocular lenses in cataract surgery and in combined vitrectomy surgery”, Br J Ophthalmol 2006; 90, 1519-1523, the contents of which are incorporated herein. Therein, a single-piece acrylic IOL was implanted in one eye of the patients and a three-piece acrylic IOL was implanted in the other eye. The recovery was documented through measuring the degree of decentration and incline of the IOL as well as the anterior chamber depth after one (1) week and monthly for up to six (6) months.
From the result, it was ascertained that there were no distinctive changes regarding mean decentration and incline but a measurement of the anterior chamber depth showed significant differences. While the anterior chamber depth did not change after implanting the single-piece IOL, a significant flattening of the anterior chamber occurred in the eyes with an implanted three-piece IOL.